Wastewater Surviellence for SARS-CoV-2 in Athens, GA
Lipp Laboratory, Department of Environmental Health Science, University of Georgia
2020-08-21
Load all required libraries.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.1 ✓ dplyr 1.0.0
## ✓ tidyr 1.1.0 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## layoutRead in raw data from RDS.
raw_data <- readRDS("./n1_n2_cleaned_cases.rds")Make a few small modifications to names and data for visualizations.
final_data <- raw_data %>% mutate(log_copy_per_L = log10(mean_copy_num_L)) %>%
rename(Facility = wrf) %>%
mutate(Facility = recode(Facility,
"NO" = "WRF A",
"MI" = "WRF B",
"CC" = "WRF C"))Create the plot:
1)Start with a basic bar plot to show Department of Public Health Data
2)Layer a line plot to show 7-day moving average
3)Layer a scatterplot to show wastewater data
4)Layer a horizontal line and annotation to indicate the limit of detection (LOD) for RT-qPCR
5)Finish with asthetics (note legend asthetics are split to maintain theme_classic)
clean_plot <- final_data %>% ggplot() +
geom_bar(aes(x = date, y = new_cases_clarke), stat = "sum", alpha = 0.35, fill = "#7570B3", show.legend = FALSE)
clean_plot <- clean_plot +
geom_line(aes(x = date, y = X10_day_ave_clarke), size = 1, color ="#E6AB02") +
geom_point(aes(x = date, y = log_copy_per_L*10, color = target, shape = Facility), size = 2.5) +
geom_hline(yintercept = 35.5, linetype = "dashed") +
annotate("text", x = as.Date("2020-03-20"), y = 39, label = "LOD")
clean_plot <- clean_plot +
scale_y_continuous(limits = c(0,80), breaks = seq(0, 80, 20),
sec.axis = sec_axis(~. / 10, name = "SARS CoV-2 Log Copies Per L")) +
ylab("Clarke County Daily Cases") + xlab("Date") +
theme_classic() + guides(color=guide_legend("SARS CoV-2 Target"))
clean_plot <- clean_plot +
theme(legend.position = "none") #generating tidy legend as png file
clean_plot <- clean_plot + scale_color_manual(na.translate = FALSE, values = c("#1B9E77", "#D95F02")) + scale_shape_manual(na.translate = FALSE, values = c(1:2))
clean_plot## Warning: Removed 2 rows containing non-finite values (stat_sum).## Warning: Removed 10 row(s) containing missing values (geom_path).## Warning: Removed 150 rows containing missing values (geom_point).
Save the plot!
ggsave(filename = "./athens_covid_wastewater_master_plot.png", plot = clean_plot)## Saving 7 x 5 in image## Warning: Removed 2 rows containing non-finite values (stat_sum).## Warning: Removed 10 row(s) containing missing values (geom_path).## Warning: Removed 150 rows containing missing values (geom_point).only_positives <<- subset(final_data, (!is.na(final_data$Facility)))
only_n1 <- subset(only_positives, target == "N1")
only_n2 <- subset(only_positives, target == "N2")
only_background <<-final_data %>%
select(c(date, cases_cum_clarke, new_cases_clarke, X10_day_ave_clarke, cases_per_100000_clarke)) %>%
group_by(date) %>% summarise_if(is.numeric, mean)
#specify fun colors
background_color <- "#7570B3"
ten_day_ave_color <- "#E6AB02"
marker_colors <- c("N1" = '#1B9E77',"N2" ='#D95F02') p1 <- only_background %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~date, y = ~new_cases_clarke,
type = "bar",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Daily Cases: ', new_cases_clarke),
alpha = 0.5,
name = "Daily Reported Cases",
color = background_color,
colors = background_color,
showlegend = FALSE) %>%
layout(yaxis = list(title = "Clarke County Daily Cases", range = c(0,80), showline=TRUE)) %>%
layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
#renders the main plot layer two as ten day moving average
p1 <- p1 %>% plotly::add_trace(x = ~date, y = ~X10_day_ave_clarke,
type = "scatter",
mode = "lines",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Ten-Day Moving Average: ', X10_day_ave_clarke),
name = "Ten Day Moving Average Athens",
line = list(color = ten_day_ave_color),
showlegend = FALSE)
#renders the main plot layer three as positive target hits
p1 <- p1 %>% plotly::add_trace(x = ~date, y = ~log_copy_per_L,
type = "scatter",
mode = "markers",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Facility: ', Facility,
'</br> Target: ', target,
'</br> Log Copies/L: ', round(log_copy_per_L, digits = 2)),
data = only_n1,
symbol = ~Facility,
yaxis = "y2",
marker = list(color = '#1B9E77', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
plotly::add_trace(x = ~date, y = ~log_copy_per_L,
type = "scatter",
mode = "markers",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Facility: ', Facility,
'</br> Target: ', target,
'</br> Log Copies/L: ', round(log_copy_per_L, digits = 2)),
data = only_n2,
symbol = ~Facility,
yaxis = "y2",
marker = list(color = '#D95F02', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
layout(yaxis2 = list(overlaying = "y",
side = "right",
title = "SARS CoV-2 Log Copies Per L",
range = c(0, 8), showline = TRUE,
automargin = TRUE)) %>%
layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
p1 <- p1 %>% plotly::add_segments(x = as.Date("2020-03-14"), xend = ~max(date + 10), y = 35.5, yend = 35.5,
opacity = 0.35,
line = list(color = "black", dash = "dash")) %>%
layout(annotations = list(x = as.Date("2020-03-27"), y = 38, xref = "x", yref = "y",
text = "Limit of Detection", showarrow = FALSE))
p1## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
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## Call `lifecycle::last_warnings()` to see where this warning was generated.## Warning: Ignoring 1 observations## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
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## Call `lifecycle::last_warnings()` to see where this warning was generated.save(p1, file = "./plotly_fig.rda")only_background_2 <- subset(only_background, date > as.Date("2020-05-20")) p2 <- only_background_2 %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~date, y = ~new_cases_clarke,
type = "bar",
alpha = 0.5,
name = "Daily Reported Cases",
color = background_color,
colors = background_color) %>%
layout(yaxis = list(title = "Clarke County Daily Cases", range = c(0,80), showline=TRUE)) %>%
layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
#renders the main plot layer two as ten day moving average
p2 <- p2 %>% plotly::add_trace(x = ~date, y = ~X10_day_ave_clarke,
type = "scatter",
mode = "lines",
name = "Ten Day Moving Average Athens",
colors = ten_day_ave_color)
#renders the main plot layer three as positive target hits
p2 <- p2 %>% plotly::add_trace(x = ~date, y = ~log_copy_per_L,
type = "scatter",
mode = "markers",
data = only_positives,
colors = marker_colors,
symbol = ~Facility,
yaxis = "y2") %>%
layout(yaxis2 = list(overlaying = "y", side = "right", title = "SARS CoV-2 Log Copies Per L", range = c(0, 8), showline = TRUE)) %>%
layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
p2save(p2, file = "./plotly_fig_2.rda")